Dental Amalgam from the Past to the Present: Utilization among Ministry of Health Dental Clinics in the Makkah Region of the Kingdom of Saudi Arabia
Bibliographic record
Abstract
Background: Amalgam fillings were invented and introduced to dentistry in France and England during the 1800s. It has since become one of the most reliable dental filling materials to treat dental caries. Dental amalgam contains approximately 50% elemental mercury, a source of occupational exposure among dental personnel and non-occupational exposure among patients. Objective: This study describes the use of dental amalgam in Makkah region dental clinics as a direct restorative material compared to composite and glass ionomer cement. Methods: This longitudinal retrospective study included 335 dental clinics in Makkah and Jeddah, the two largest cities in the Makkah region, Kingdom of Saudi Arabia. Annual statistical data were obtained from the Directorate of Dentistry, Makkah and Jeddah Health Affairs, Ministry of Health. Data related to the restorative materials used (composite, glass ionomer cement (GIC), and amalgam) were counted for 11 years starting from 2009 to 2019 for Makkah city, and the restorative materials used (composite, GIC, and amalgam) from 2018 to the first quarter of 2021 for Jeddah city. Results: There was a slight increase in the number of amalgam restorations in Makkah from 2009 (37.15%) to 2011 (43.52%), followed by a gradual decrease until 2019 (1.39%). In Jeddah, there was a slight increase in amalgam restorations from 2018 (9.39%) to 2019 (11.03%). However, the use of amalgam restorations reduced sharply in 2020 (3.27%) and in the first quarter of 2021 (3.53%). Conclusion: There is a recognizable decreased trend in amalgam utilization in the Makkah region. Amalgam use is being phased down despite the lack of official regulation on its use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".